DataRobot-Research/drdocs huggingface dataset:
# Version 7.2.2 {: #version-722 }

_September 28, 2021_

The DataRobot v7.2.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:

* [Features introduced in v7.2.0](v7.2.0-aml)

## Issues fixed in v7.2.2 {: #issues-fixed-in-v722 }

The following issues have been fixed since Enterprise release v7.2.1:

### Feature Discovery {: #feature-discovery}

* SAFER-4224: Fixes an issue causing EDA to become deadlocked when using python3 and the fork method. This fix bumps the version of the joblib external dependency to pick up a fix for the semaphore tracker and changes python3 to use forkserver process start method.

### Predictions {: #predictions}

* CODEGEN-1097: Fixes an issue where old, unused JAR files were installed. Old jar files have been removed from the installation.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Modeling reference
description: Introduces sections that provide a deep dive into aspects of DataRobot functionality, including Data and sharing, Modeling details, Eureqa advanced tuning.
---

# Modeling reference {: #modeling-reference }

The topics in these section provide a deep dive into aspects of DataRobot functionality.

Topic | Describes...
----- | ------
[Data and sharing](data-sharing/index) |  Dataset requirements, sharing assets, and permissions.
[Modeling details](model-detail/index) | Understand the stages of the model building process as well as SHAP details.
[Eureqa advanced tuning](eureqa-ref/index) | Modify building blocks, customize the target expression, and modify other model parameters for Eureqa models.
---
title: Deploy a model in AWS SageMaker
description: Learn how to programmatically build a model with DataRobot and export and host the model in AWS SageMaker

---

# Deploy a model in AWS SageMaker {: #deploy-a-model-in-aws-sagemaker }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/tree/main/end-to-end/sagemaker_deployment){ .md-button }

In this accelerator, you deploy a model that has been built in DataRobot to AWS SageMaker. If you already use SageMaker for hosting models, you can still make use of the powerful features of DataRobot, including AutoML and time series modeling. You can integrate DataRobot into your existing deployment processes. Likewise, you can use this workflow to deploy a DataRobot-built model into another type of environment.

In this accelerator you will follow the manual steps that are [outlined in DataRobot's documentation](sc-sagemaker#use-scoring-code-with-aws-sagemaker), programmatically build a model with DataRobot, and export and host the model in AWS SageMaker. To assist with the setup of AWS services to run the model, this code provisions any extra items that you may not haven yet set up.

Review the lists below of what is created in this AI accelerator.

### AWS

* ECR Repository
* S3 Bucket
* IAM Role for SageMaker
* SageMaker inference model
* SageMaker endpoint configuration
* SageMaker endpoint (for real time predictions)
* SageMaker batch transform job (for batch predictions)

### DataRobot

* DataRobot AutoML Project
* DataRobot AutoML Models
* Scoring Code JAR file of AutoML Model

Once you have run through the code, you will see how you can leverage the power of DataRobot's automated machine learning capabilities to train a model and then make use of the power of AWS to deploy and host that model in SageMaker.
---
title: Account and project management
description: This section introduces the management toolbar and includes links to information on how you can manage account settings.
---

# Account and project management {: #account-and-project-management }

The information in this section provides information to help manage your DataRobot account using the management toolbar&mdash;the navigation elements in the upper right&mdash;to access many account and project management tools.

![](images/help-toolbar.png)

  &nbsp; | Option  |  Description
 ------ | --------------------------------- | ----------------------------------
 ![](images/icon-1.png) | [Manage projects](manage-projects) | Opens the page for starting a new project and provides a link to your existing project repository.
 ![](images/icon-2.png) |  [Resources](getting-help) | Provides access to DataRobot UI and API documentation, Enterprise Support, and the DataRobot Community. Additionally, it allows you to send suggestions and issues to DataRobot.
 ![](images/icon-3.png) | [Notifications](user-notif-center) | Opens a modal that lists notifications sent from the DataRobot platform.
 ![](images/icon-4.png) | [Account settings](acct-settings/index) | Provides access to profile information, [two-factor authentication](2fa) and other settings, data sources, and your [membership assignments](view-memberships).
 ![](images/icon-5.png) |[Value Tracker](value-tracker) | Specify what you expect to accomplish by using DataRobot, and share.
  ![](images/icon-6.png) |[Pricing](value-tracker) | Learn which DataRobot features are accessible to each pricing plan.
---
title: Troubleshooting
description: View common issues and troubleshooting tips for a smooth DataRobot experience.
---

# Troubleshooting {: #troubleshooting }

This section provides information on troubleshooting DataRobot authentication and access:

Topic | Describes...
----- | ------------
[Trial FAQ](trial-faq) | Questions and answers about the DataRobot Self-Service SaaS trial.
[Signing in](signin-help) | Things to try if you are having issues signing in.
[Two-factor authentication](2fa) | Common issues when working with 2FA.
[Check platform status](status-help) | View and subscribe to platform status announcements.
[Number of workers](workers-help) | Learn about increasing the worker count.
[Troubleshooting the Python client](py-help) | Review cases that can cause issues with using the Python client and known fixes.
---
title: End-to-end ML workflow with Databricks
description: Build models in DataRobot with data acquired and prepared in a Spark-backed notebook environment provided by Databricks.

---

# End-to-end ML workflow with Databricks {: #end-to-end-ml-workflow-with-databricks }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/Databricks_End_To_End.ipynb){ .md-button }

DataRobot features an in-depth API that allows data scientists to produce fully automated workflows in their coding environment of choice. This accelerator shows how to pair the power of DataRobot with the Spark-backed notebook environment provided by Databricks.

In this notebook you'll see how data acquired and prepared in a Databricks notebook can be used to train a collection of models on DataRobot. You'll then deploy a recommended model and use DataRobot's exportable Scoring Code to generate predictions on the Databricks Spark cluster.

This accelerator notebook covers the following activities:

* Acquiring a training dataset.
* Building a new DataRobot project.
* Deploying a recommended model.
* Scoring via Spark using DataRobot's exportable Java Scoring Code.
* Scoring via DataRobot's Prediction API.
* Reporting monitoring data to DataRobot's MLOps agent framework.
* Writing results back to a new table.
---
title: Get started
description: Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-first accelerators and notebooks.

---

# Get started {: #get-started }

Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-first accelerators and notebooks.

Topic          | Describes...
---------------|---------------
[DataRobot in 5](gs-dr5/index) | Understand the 5 basic steps to building and deploying AI models.
[Workbench](gs-workbench/index)    | Understand the components of Workbench, work with data, build and explore models; compare Workbench capabilities with DataRobot Classic.
[DataRobot Classic](gs-classic/index)   |  Understand modeling project types, prepare modeling data, build and explore models, and deploy, monitor, and manage models in production.
[Work with notebooks](gs-code) | Learn how to get started coding with DataRobot and how to leverage AI accelerators to quickly engaging in code-first machine learning workflows.
[Get help](gs-get-help/index) | Review troubleshooting tips and view quick, task-based instructions for success in modeling.
# Version 8.0.14 {: #version-8014 }

_April 24, 2023_

The DataRobot v8.0.14 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

In addition to the issues listed below, DataRobot addressed several outstanding security issues. This release/update is part of DataRobot's continual effort to secure the product and platform.

## Issues fixed in v8.0.14 {: #issues-fixed-in-v8014 }

The following issues have been fixed since release v8.0.13:

### Core modeling {: #core-modeling }

- MODEL-11218: Fixes an issue where Keras models tuned in the API with a single layer resulted in an error.

### MLOps {: #mlops }

- AGENT-4288: Deletes an unnecessary import line at the beginning of the MLOps agent code snippet that caused an import error.
- MMM-12255: Adds `Project ID` and `Project Name` to User Activity Monitor entries for "Deployment Add" events.
- MMM-12742: Marks manually triggered retraining policy runs as failed when encountering a credential error.
- MMM-12898: Fixes the target histogram for external deployments built inside DataRobot.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Time series reference
description: This section provides deep-dive reference material for DataRobot time series modeling.

---

# Time series reference {: #time-series-reference }

This section provides deep-dive reference material for DataRobot time series modeling.

Topic | Describes...
----- | ------
[Time series framework](ts-framework) | The framework DataRobot uses to build time series models.
[Time series feature derivation](feature-eng) | The feature derivation process and intermediate and final features.
[Autopilot in time-aware projects](multistep-ta) | Modeling modes as they apply to time aware projects.
[Time series feature lists](ts-feature-lists) | Details of the feature lists used for time series modeling.
Multiseries with segmentation | An [FAQ](segmented-faq) and [visual example](segmented-qs) to help understand multiseries segmentation.
[Feature considerations](ts-consider) | Considerations to keep in mind when working with time-aware modeling.
---
title: Algorithmia
description: Algorithmia is an MLOps platform where you can deploy, govern, and monitor your models as microservices.
---

# Algorithmia

DataRobot Algorithmia is an MLOps platform where you can deploy, govern, and monitor your models as microservices. The platform lets you connect models to data sources and deploy them quickly to production. See the [Algorithmia Developer Center](https://algorithmia.com/developers) for details.
---
title: No-Code AI App reference
description: Reference material for No-Code AI Apps in Workbench, including considerations and frequently asked questions (FAQs).
---

# No-Code AI App reference {: #no-code-ai-app-reference }

## FAQ {: #faq }

??? faq "How do I see which model an app was built from?"
    In your Use Case, go to the [**Applications**](wb-apps/index#applications-tab) tab. The **Source** column lists the model each app was created from.


??? faq "Is there a way to see how many predictions were made using an app?"
    You can view every prediction made in an app in the All Rows widget or by downloading them as a CSV.


??? faq "Can I re-run an optimization in the Optimizer app after changing the widget's settings?"
    At this time, you cannot re-run an optimization unless you recreate the prediction row.
## Sample paths {: #sample-paths }

See the following paths for examples of adding and working with data in Workbench:

=== "via a data connection"

    ``` mermaid
    flowchart TB
      A(Add new > Add datasets) --> B(Connect to Snowflake);
      B --> C(Preview a dataset);
      C --> E(Does it need to be prepared?);
      E --> | No | F(Add to Use Case);
      E --> | Yes| G(Wrangle);
      G --> H(Build and publish a recipe);
      H --> I(View Exploratory Data Insights);
      F --> I;
      I --> J(Is it ready for modeling?);
      J --> | No | G;
      J --> | Yes | K(Set up an experiment);
    ```

=== "via a local file"

    ``` mermaid
    flowchart TB
      A(Add new > Add datasets) --> B(Upload);
      B --> C(Select locally-stored file);
      C -- Registers in the Data Registry + Adds to Use Case --> E(View Exploratory Data Insights);
      E --> G(Set up an experiment);
    ```

=== "via the Data Registry"

    ``` mermaid
    flowchart TB
      A(Add new > Add datasets) --> B(Browse the Data Registry);
      B --> C(Preview a dataset);
      C --> D(Is it appropriate for the Use Case?);
      D --> | No | B;
      D --> | Yes | E(Add to Use Case);
      E --> F(View Exploratory Data Insights);
      F --> G(Set up an experiment);
    ```
---
title: Create custom blueprints with composable ML
description: Customize models on the Leaderboard using the Blueprint Workshop.

---

# Create custom blueprints with composable ML {: #create-custom-blueprints-with-composable-ml}

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced-experimentation/custom_blueprints/create_custom_blueprint.ipynb){ .md-button }

DataRobot's [Composable ML](cml/index) allows you to add pre-defined tasks to a blueprint or to insert their own custom code. You're free to add your data science and subject matter expertise to the models you build.

This accelerator shows how to customize the models on the leaderboard via Composable ML's API, the Blueprint Workshop. It covers the following activities:

* Access the Blueprint Workshop
* Define and train a custom blueprint using the tasks provided by DataRobot
* Insert custom code in the form of a CatBoost classifier into the blueprint
---
title: V8.0.x maintenance releases
description: Maintenance releases for the DataRobot Release 8.0 major release.
---

# V8.0.x maintenance releases {: #v80x-maintenance-releases }

The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platform. See also the [features introduced in v8.0.0](v8.0/index), which was introduced _March 14, 2022_.

Version |	Release date
------- | -----------
* [v8.0.16](v8.0.16-aml) |  *June 26, 2023*
* [v8.0.15](v8.0.15-aml) | *May 22, 2023*)
* [v8.0.14)](v8.0.14-aml) | *April 24, 2023*
* [v8.0.13)](v8.0.13-aml) | *March 27, 2023*
* [v8.0.12)](v8.0.12-aml) | *February 27, 2023*
* [v8.0.11)](v8.0.11-aml) | *January 23, 2023*
* [v8.0.10)](v8.0.10-aml) | *December 19, 2022*
* [v8.0.9)](v8.0.9-aml) | *November 14, 2022*
* [v8.0.8)](v8.0.8-aml) | *November 7, 2022*
* [v8.0.7)](v8.0.7-aml) | *September 30, 2022*
* [v8.0.6)](v8.0.6-aml) | *August 29, 2022*
* [v8.0.5)](v8.0.5-aml) | *July 29, 2022*
* [v8.0.4)](v8.0.4-aml) | *June 29, 2022*
* [v8.0.3)](v8.0.3-aml) | *May 19, 2022*
* [v8.0.2)](v8.0.2-aml) | *April 30, 2022*
* [v8.0.1)](v8.0.1-aml) | *March 30, 2022*
---
title: Deploy models
description: How to create deployments from DataRobot models, custom inference models, and external models.

---

# Deploy models {: #deploy-models }

In DataRobot, the way you deploy a model to production depends on the type of model you start with and the prediction environment where the model will be used. The following sections describe how to add deployments for different types of artifacts, including models built in DataRobot, custom inference models, and remote models. 

=== "SaaS"

	Topic | Describes 
	-------|--------------
	[Deploy DataRobot models](deploy-model) | How to deploy DataRobot models from the Leaderboard or the Model Registry.
	[Deploy custom inference models](deploy-custom-inf-model) |  How to deploy custom inference models from the Custom Model Workshop.
	[Deploy external models](deploy-external-model) |  How to deploy external (remote) models from the Model Registry or by uploading training data and deploying from the deployment inventory.
	[MLOps agents](mlops-agent/index) |  How to monitor and manage deployments running in an external environment outside of DataRobot MLOps.
	[Configure a deployment](add-deploy-info) | How to complete deployments by configuring inference options.
	[Add prediction data post-deployment](add-prediction-data-post-deploy) | How to add historical prediction data to existing deployments. 

=== "Self-Managed"

	Topic | Describes
	-------|--------------
	[Deploy DataRobot models](deploy-model) | How to deploy DataRobot models from the Leaderboard or the Model Registry.
	[Deploy custom inference models](deploy-custom-inf-model) |  How to deploy custom inference models from the Custom Model Workshop.
	[Deploy external models](deploy-external-model) |  How to deploy external (remote) models from the Model Registry or by uploading training data and deploying from the deployment inventory.
	[MLOps agents](mlops-agent/index) |  How to monitor and manage deployments running in an external environment outside of DataRobot MLOps. 
	[Configure a deployment](add-deploy-info) | How to complete deployments by configuring inference options.
	[Add prediction data post-deployment](add-prediction-data-post-deploy) | How to add historical prediction data to existing deployments.
	[Imported `.mlpkg` file](reg-transfer) |  How to import and deploy .mlpkg files from the Model Registry.
---
title: Ordered categoricals
description: With DataRobot's monotonicity, to normalize or not to normalize, that is the question.
---

# Ordered categoricals {: #ordered-categoricals }

<span style="color:red;font-size: 1rem"> `Robot 1`</span>

Does DataRobot recognize ordered categoricals, like grade in the infamous lending club data?

This is a question from a customer:

> Can you tell your models that `A < B < C` so that it’s more regularized?

I feel like the answer is that to leverage ordering you would use it as numeric feature. Quite likely a boosting model is at the top, so it’s just used as an ordered feature anyway.
If you just leave it as is, our models will figure it out.

When using a generalized linear model (GLM), you would want to leverage this information because you need fewer degrees of freedom in your model; however, I'm asking here to see if I missed some points.

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

We actually do order these variables for XGBoost models. The default is frequency ordering but you can also order lexically.

<span style="color:red;font-size: 1rem"> `Robot 1`</span>

You mean ordinal encoding or directly in XGBoost?

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

Yeah the ordinal encoding orders the data.

<span style="color:red;font-size: 1rem"> `Robot 1`</span>


![](images/rr-order-cat-1.png)

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

Just change frequency to `lexical` and try it out.

<span style="color:red;font-size: 1rem"> `Robot 3`</span>

![](images/rr-order-cat-2.png)

Build your own blueprint and select the cols &mdash;explicitly set to `freq/lex`.

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

If you’re using a GLM, you can also manually encode the variables in an ordered way, (outside DR):

Use 3 columns:

	A: 0, 0, 1
	B: 0, 1, 1
	C: 1, 1, 1

Lexical works fine in a lot of cases; just do it for all the variables. You can use an `mpick` to choose different encodings for different columns.


<span style="color:red;font-size: 1rem"> `Robot 1`</span>

Got it! Thanks a lot everyone!
---
title: Integrate DataRobot and Snowpark by maximizing the data cloud
description: Use Python functions to aggregate DataRobot model insights into visualizations.

---

# Integrate DataRobot and Snowpark by maximizing the data cloud {: #integrate-datarobot-and-snowpark-by-maximizing-the-data-cloud }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/snowflake_snowpark/Native%20integration%20DataRobot%20and%20Snowflake%20Snowpark-Maximizing%20the%20Data%20Cloud.ipynb){ .md-button }

If you or your team have tried to develop and productionalize machine learning models with Snowflake using Python and Snowpark but are looking to level up your end-to-end ML lifecycle on the data cloud, then this AI Accelerator is for you.

Depending on your role within the organization,

This accelerator can address a number of use cases:

* Providing technical personnel with a hosted notebook.
* Create an improved developer experience.
* Improve monitoring capabilities for models within Snowflake.
* Provide guidance and insights for business personnel who want action items: next steps for customers, sales, marketing, and more.

DataRobot addresses these exact needs, which can be found in this notebook. In addition, it is compatible with the Snowflake data science stack and DataRobot 9.0 to give you advantages in terms of speed, accuracy, security, and cost-effectiveness.
---
title: Cold start demand forecasting workflow
description: This accelerator provides a framework to compare several approaches for cold start modeling on series with limited or no history.
---

# Cold start demand forecasting workflow {: #cold-start-demand-forecasting-workflow}

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/Demand_forecasting_cold_start/End_to_end_demand_forecasting_cold_start.ipynb){ .md-button }

The cold start demand forecasting problem refers to the challenge of predicting future demand for a new product or service with little or no historical sales data available. This situation typically arises when a company introduces a new product or service to the market or a new product is launched in a store that is already being sold in other stores, and there is no past data available for training a machine learning model to predict future demand.

In traditional demand forecasting, historical sales data is used to train a machine learning model that can predict future demand. However, in the case of a new product, there is no historical data available. This presents a significant challenge because accurate demand forecasting is critical for making informed decisions about inventory, pricing, and marketing strategies.

This second accelerator of a three-part series on demand forecasting provides the building blocks for cold start modeling workflow on series with limited or no history.  This accelerator provides a framework to compare several approaches for cold start modeling.

The previous notebook aims to inspect and handle common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation with the tools mentioned above and more.

The dataset consists of 50 series (46 SKUs across 22 stores) over a 2 year period with varying series history, typical of a business releasing and removing products over time. The test dataset contains 20 additional series with little or no history which were not present in the training dataset.